Numerous hydrological, anthropogenic, and geophysical factors can cause landslides, which are complicated natural disasters. Accurately predicting these occurrences is crucial to minimize the loss of property and life. Predefined predictive models now use machine learning algorithms and remote sensing data to identify landslide-prone areas thanks to advancements in data science. To start creating reliable classification models, this method combines historical landslide records, topographical data, lithology, rainfall data, and satellite imagery.

Digital elevation models (DEM), rainfall intensity, slope angle, soil type, vegetation indices, and land use patterns are just a few examples of the immense quantities of spatial and temporal data that are used in these models. To categorize regions as stable or prone to slope failures, machine learning algorithms such as Random Forest, Support Vector Machines (SVM), Gradient Boosting, and Artificial Neural Networks (ANN) are trained on historical landslide data.

In order to interpret risk zones, predefined models are commonly integrated with Geographic Information Systems (GIS) and frequently incorporate data from multiple sources, such as satellite imagery and sensor data. In disaster management, risk reduction, urban planning, and infrastructure development, this approach enables data-driven, real-time decision-making.